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• Published: 17 February 2021

Urbanization and economic complexity

• Riccardo Di Clemente   ORCID: orcid.org/0000-0001-8005-6351 1 , 2 ,
• Emanuele Strano 3 &
• Michael Batty 2  

Scientific Reports volume  11 , Article number:  3952 ( 2021 ) Cite this article

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• Applied mathematics
• Complex networks
• Statistical physics

Urbanization plays a crucial role in the economic development of every country. The mutual relationship between the urbanization of any country and its economic productive structure is far from being understood. We analyzed the historical evolution of product exports for all countries using the World Trade Web with respect to patterns of urbanization from 1995 to 2010. Using the evolving framework of economic complexity, we reveal that a country’s economic development in terms of its production and export of goods, is interwoven with the urbanization process during the early stages of its economic development and growth. Meanwhile in urbanized countries, the reciprocal relation between economic growth and urbanization fades away with respect to its later stages, becoming negligible for countries highly dependent on the export of resources where urbanization is not linked to any structural economic transformation.

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Introduction.

It is an established fact that urbanization in developed countries is accompanied by economic growth and industrialization which mutually self-reinforce one another 1 . This historic pattern generates an expectation of a virtuous circle between economic growth and urbanization regardless of local conditions 2 , 3 . From classic urban economic theories 4 , 5 to the more recent scaling approach to cities 6 , 7 , the growth of urban population has routinely been used as a proxy for economic growth. This pattern has also been observed in rapidly developing countries such as China and India but it cannot be considered a universal blueprint 8 for deviations from this norm have not yet been fully explained.

In fact, as pointed out in several studies 9 , 10 , 11 , 12 , the increasing urbanization rate in persistently poor and non-industrialized countries poses an important dilemma for urban economic theory. Why, given the same rate of urbanization, does Asia contain a number of explosive economies while sub-Saharan Africa has seen very little growth? Moreover, in developed and advanced industrialized economies, is there appears to be a competitive advantage in continuing this urbanization process indefinitely?

There are several theories aimed at explaining urbanization processes. Some argue that rural poverty moves people to cities as was clearly the case in nineteenth century Europe and America 13 , driving the transformation from an agricultural to an industrial-service based economy 14 , 15 . Others argue that in the last decades there has been urban-biased public policy that has led to over-urbanization 12 .

The most intriguing approach however is rooted in the mutual indirect effects of the World Trade Web (WTW) on global urbanization 16 , 17 . The dominant idea is that in open economies, domestic communities (such as cities) can trade easily with other communities, boosting their exports in substituting industrialization for urbanization policy 18 . In simple terms, the commodities can flow more freely using urban agglomerates as nodes in the trading networks between countries, generating the ever present virtuous circle between economic growth and urbanization.

Starting from this theory, we analyze the WTW to explore the mutual relationship between the urbanization of the countries and their economic production structure using the Economic Complexity (EC) framework. Economic Complexity 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , is a new and expanding field in the economic analysis, which proposes “Fitness” and “Complexity” metrics to quantify the fitness or competitiveness of countries and the complexity of products from a country’s basket of exports. The main focus of EC is based on a bipartite representation of the World Trade Web where the nodes represent the set of world-countries and the set of exported products defined as different entities. Countries and products are connected to one another by imposing a threshold based on their Revealed Comparative Advantage (RCA) 28 which defines the criterion for the existence of relations.

The Fitness and Complexity algorithm is a kind of PageRank method applied to WTW, where Fitness \(F_c\) is the quantity for country c , and Complexity \(Q_p\) is the quantity for products p . The idea at the basis of the algorithm is that the countries with the highest fitness are those which are able to export the highest number of the most exclusive products i.e. those with the highest complexity. On the other hand this complexity is non linearly related to the fitness of its exporters so that products exported by low fitness countries have a low level of complexity and high complexity products are exported by high fitness countries only.

The Fitness metric is valuable in quantifying a country’s productive structure and structural transformations which enable one to predict its future economic growth 23 . It correlates with the extent of economic equality 29 and it has been used to analyze the country’s growth path to industrialization 30 .

In this work, we used a data driven approach borrowing tools recently introduce by statistical physics and network science to improve our understanding of the complex dynamics of human societies, with the aim of finding innovative insight 31 to link urbanization process with the evolution of the international trade.

We couple the WTW data with the urbanization level of more than 144 countries worldwide, and analyze this between 1995-2010 thus capturing the fingerprint of urbanization on countries’ productive systems through the lens of their exports. We notice that in rural economies, the increase in urban population fosters structural changes in industrial exports. It boosts the country’s diversification improving the country fitness, and allowing the export of more complex products. These economic transformations fade away in countries that already have a high level of urban population (more than \(60\%\) ) where there is no relation between the urbanization process and the country’s fitness.

Within the sub-Saharan countries, we capture those where the virtuous circle between economic growth and urbanization is fostering structural changes in those countries’ productive systems. On the other hand within countries with economies based on raw materials, we assess the implementation of policy leading to urbanization that does not support any structural transformations of their basket of exports.

Economic complexity and urbanization

We represent the WTW as a bipartite network, i.e. by considering the set of world-countries and the set of products as different entities and linking a given country to a given product if (and only if) the former exports to the latter above a certain threshold (the so-called Revealed Comparative Advantage—RCA) 28 . RCA is a general criterion adopted in order to understand whether a country can be considered, or not, a producer of a particular product. It quantifies how much the export of a given product p is relevant for the economy of a country c in relation to the global export of p for all countries (See “ Methods ” Section).

The country’s fitness and product’s complexity are the result of a non-linear iterative map applied to the WTW matrix M 19 , 20 , 32 (See “ Methods ”).

Through the algorithm’s iterations, products exported by low fitness countries have a low level of complexity while high complexity products are exported by high fitness countries only. The countries’ composition of their export basket depends on their fitness. Fitness and Complexity are thus non-monetary indicators of the economy’s development: the fitness represents a measure of tangible and intangible assets and capabilities, which drive the country’s development, such as political organization, its history, geography, technology, services, and infrastructures 21 . Meanwhile complexity measures the necessary capabilities which must be owned by a country in order that it can produce and then export the resulting product.

Within this framework, we include the dimension of a country’s degree of urbanization defined as the percentage of the total population living in urban areas. Our aim is to quantify the link between a country’s urbanization process and their exports as a proxy for their industrial economic system. To disentangle the relation between country productivity systems and their urbanization, we have divided the set of countries in terms of their degree of urbanization, defined by the Urban Range, which is expressed in four quantiles [ Q 1,  Q 2,  Q 3,  Q 4] (see urban range division in Fig.  1 B top).

( A ) Distribution of exported products complexity by different urbanization levels through the 2000–2010. There is a shift of lower urbanized countries towards the export of more complex products ( B ) Distribution of the Urban Range (percentage of the total population living in urban areas) of the 144 countries analyzed. ( B ) Ranked country fitness versus products export diversification, the highly diversified countries are one’s with more fitness and high urbanization, meanwhile low urbanized country are in the center bottom of the scatter plot, with some exceptions such as those with links to the oil countries. ( C ) Matrix of the country exports in 2010, reordering the countries and products by fitness and complexity; the color dots represent an exported product under the RCA threshold Eq. ( 2 ), the color gradient follows the urban range definition.

More urbanized countries [Q3,Q4] in the early 2000s, export a wide range of complex products such as: textiles, heavy manufacturing industries, and IT while rural countries [Q1,Q2] export products that require a low level of sophistication such as raw materials and agricultural products (Fig.  1 A).

Highly urbanized countries maintain a similar distribution across the analysis years, with a long tail of low complexity products and a consistent increase in the number of high complexity products. On the other hand starting from 2005, we have noticed that rural countries change their export basket towards higher complexity products. This shift is shown by the cumulative distribution functions of the different Urban ranges that decrease their distance from one another over time (see Fig.  1 A inset) together with their median and peak distance.

We notice that countries within the higher quantile of the Urban Range, Fig.  1 B, are the ones with higher fitness and higher diversification, whilst low urbanized countries have a low diversification and fitness. Notable exceptions are countries with exports based on raw materials (i.e. Qatar, Kuwait, Gabon, Iraq, Libya). These countries reached higher levels of urbanization as result of policy decisions 33 meanwhile their exports are limited to a few products with low complexity.

The representation of the WTW in Fig.  1 C shows country exports in 2010 rearranged by ranked fitness and complexity. The country exports’ diversification is related to the urbanization level. Low urbanized countries are at the bottom of the matrix with low fitness and lower degree of diversification, whilst the urbanized countries, with the most advanced economies lie at the top, with a high degree of product diversification with different levels of complexity and high fitness.

Exports diversification and urbanization

It is known that low fitness countries have similar economies with low degrees of diversification and high similarity with respect to their export baskets 20 , 34 , 35 i. e. they produce and export few of the same low technology products. We captured a shift in the distribution of the exported products within the rural countries (Fig.  1 A). In particular, we noticed that rural countries start to produce and export more sophisticated products. This productive systems transformation in the EC literature is related to the development of new capabilities 22 , 36 , 37 .

Some questions from this analysis emerge: do the rural countries evolve their productive systems in the same way? and do they continue to produce and export the same products? Is the pattern of economic development entangled with urbanization in same fashion for each rural country?

We can measure indirectly the transformation of the productive systems by analyzing the evolution of WTW topology 38 . In particular we can assess the changes of countries’ similarities in their exports studying the abundance evolution of network motifs 39 . A network motif is a particular pattern of interconnections occurring between the nodes of the network (i.e. between the countries and their products). In our case we are interested in the abundance of the similarity motif \(\mu _{sim}\) in Fig.  2 B (motifs 6 40 , or X motif 35 ): it quantifies the co-occurrence of any two countries as producers of the same couple of products as Eq. ( 7 ) (and, viceversa, the co-occurrence of any two products in the basket of the same couple of countries). This represents the simplest motifs 40 that can quantify the similarities in the export countries’ diversification which maintains a pairwise correlation within the products exported. Two economies with a fixed number of products exported are diversifying if the values of \(\mu _{sim}\) is decreasing while their production similarity increases with high values of \(\mu _{sim}\) .

( A ) Z-score of the export similarity motif by country groups with different Urban Ranges and the Z-score of the whole WTW in black. ( B ) Similarity motif as the co-occurrence of any two countries as producers of the same couple of products 35 , 40 .

To provide a benchmark and asses the \(\mu _{sim}\) statistical significance of the WTW we use the Bipartite Configuration Model (BiCM) 34 as a null model. This framework is valuable in the analysis of the abundance of the bipartite motifs 40 , enabling us to detect financial crisis effects on a country’s export basket 35 as well as export similarities between countries with same level of economic development 41 .

We generated 1000 matrices using the BiCM 34 (see “ Methods ” Section) and we compare the observed abundances of the similarity motif (Eq. 7 ) in the real network with the corresponding expected values in the null ensemble using the Z-score.

The whole WTW manifests a progressive increase of the abundance of similarity motifs with respect to the null case 35 (Black line Fig.  2 A). Highly urbanized countries show a similar trend of increasing similarity in their products exports. This measure implies that rural economies are very similar with a higher abundance of the similarity motif with respect to the random case having a high value Z-score. Interestingly, low urban range countries diversifying between each other manifest an opposite trend. The exports diversification trends of the low urbanized countries coupled with the increasing complexity of the product exported imply a nontrivial connection between urbanization and production capabilities. This measure outlines how rural economies follow different development patterns based on their production systems. The urbanization phenomenon coupled with the capabilities already presented in the country enable the production of different sophisticated products depending on their environment.

Urbanization growth and country fitness

The economic transformation of a rural country has an impact on its overall fitness value, and the competitiveness of its productive system. In this respect, the urbanization process is key element in a country’s development and its economic growth 33 , 42 . To assess the relation between the country’s fitness and the urbanization process we analyzed the Urban Range growth rate in relation to the growth rate of country fitness ranking between 1995 and 2010, as we show in Fig.  3 . The country fitness ranking is the country’s ordered position with respect to the country’s fitness value in a given year. The growth rate of the country’s fitness ranking is an easily understood tool to compare the transformations of a country’s productive systems with respect to its competitors. It has been proven a reliable tool in quantifying the country’s relative degree of competitiveness across different years providing a more stable measurement than the raw fitness value 43 .

( A ) The Fitness Ranking Growth Rate versus Urbanization Growth Rate. The effect of urbanization growth on the transformation of the economic systems (or vice-versa) is more relevant in low urbanize countries. The dashed lines represent the 95% Confidence Interval (CI) of the linear regression. ( B ) Slope coefficient of a sliding window across \(25\%\) of the countries (corresponding to 36 countries) of its fitness ranking growth rate versus urban population growth rate. The error bar corresponds to the fit’s \(95\%\) confidence interval. The colors follow the Urban Range Scheme.

For each of the four Urban Range quantiles we find a linear relation between the urbanization rate and the Fitness ranking growth rate in Fig.  3 b. Increasing urbanization within lowly urbanized countries is interwoven with increasing Fitness. Meanwhile, the effects are minimal in highly urbanized countries (Urban Range Q3,Q4). We validate the urbanization/fitness relation analyzing a \(25\%\) quantile sliding window on the whole urbanization distribution, which we show in black in Fig.  3 B.

We notice that in many rural economies, the urbanization process affects or has been affected by structural changes in its economic production. (An example are countries such as Uganda, Nepal, Somalia.) On other hand, there are many countries (such as IvoryCoast, Paraguay, Chad) where the urbanization process does not provide improvement in the fitness ranking 44 .

The self-reinforced mechanism between urbanization and fitness reaches a plateau within the urbanized countries (Q3,Q4), where the urbanization does not affect or has not been affected by changes in fitness ranking. In this respect, the resource exports countries manifest a shift toward a negative relation between urbanization and fitness. In fact in countries that are heavily dependent on resource exports, urbanization appears to be concentrated in the cities where the economies consist primarily of non-tradeable services 45 . To support our result we provide the same analysis using instead of the Fitness Ranking metric, the Fitness, Gross Domestic Product (GDP) and GDP Ranking respectively (see “ Methods ” section: Urban Range vs Fitness and GDP). We do not find any evidence of relation between the other three metrics and the urbanization rate.

Urban fitness trends

The process of urbanization is often entangled with a country’s industrialization 11 . As countries develop, people move out of rural areas and agricultural activities into urban centers, where they engage in manufacturing products 46 which are more sophisticated with higher complexity. This transformation is outlined by the increasing level of fitness of low urbanized countries that are involved in the urbanization process. To leverage this information and capture its trends, we define the country Urban Fitness \(F_{c}^{\text{ urb }}(t)=F_c(t)*U_c(t)\) ; this is the value of country fitness \(F_c\) weighted by the percentage of urban population \(U_c\) .

( A ) Clusters of normalized Urban Fitness Trends. ( B ) Correlation Matrix of the countries urban fitness trends clustered with the Louvain algorithm. ( C ) Geographical cluster distribution. The map in this figure was created using the software QGIS.

We cluster the countries Urban Fitness trends using the Louvain algorithm 47 which is based on their correlation matrix shown in Fig.  4 B. Three clusters emerge with high correlations disentangling the non-trivial geographical relations we show in Fig. 4 A–C.

In Fig.  4 A countries with a clear urbanization trend (in orange) are ones with a stable increase in fitness ranking. Meanwhile the blue cluster contains developed countries, where the urbanization does not provide any new input to the economic development and resource dependent countries, where the urbanization is not only lead by deep structural economic change. These results are in agreement with the Urban Range study in Fig.  3 that show a poor effect of the urbanization on the country fitness, implying that over a given value of urbanization, other factors have a more important role in economic development and growth. Finally, the third cluster (in red) are the countries without any clear trend and are thus uncategorized.

It is well-known that urbanization provides several advantages to the economics of scale and division of labour, boosting productivity and competition. It helps in accessing the labor force and inputting materials to the production process as well as decreasing the geographical distance between firms, reducing transaction costs, and fostering competition 48 . These urbanization advantages 49 together with the appropriate bureaucratic environment 33 , investment in infrastructures 50 and companies market structure 51 , are some of intangible attributes, the capabilities, that a country needs to drive economic growth and innovation 36 . We noticed that the country Fitness, the production and export of goods, is interwoven within the urbanization process during the early stages of country’s economic development and growth. We show that the information carried by WTW can provide a different perspective on analyzing the complex process of urbanization, enlightening the relation between a country’s exports, economic development and its urban growth.

World trade web

The dataset used in this work is the BACI (Base pour l’Analyse du Commerce International) World Trade Database (Gaulier, S. Baci: International trade database at the product-level http://www.cepii.fr/CEPII/fr/publications/wp/abstract.asp?NoDoc=2726 Date of access: 18/01/2021). The data contains information on the trade of 200 different countries for more than 5000 different products, categorized according to the 6-digit code of the Harmonised System 2007 ( http://www.wcoomd.org/ Date of access: 18/01/2021). The products’ sectors follows the UN categorization ( http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=8 Date of access: 18/01/2021). We create a map between the two systems converting the HS2007 in to the ISIC revision 2 code at 2-digit ( http://www.macalester.edu/research/economics/PAGE/HAVEMAN/Trade.Resources/TradeConcordances.html#FromISIC Date of access: 18/01/2021). We represent the trade relation between the 144 countries \(c\in [1,C]\) and the 1131 products \(p\in [1,P]\) between the years [1995, 2010] throught the bipartite matrix \({\tilde{M}}\) with dimension \((C\times P)\) where each entry \({\tilde{m}}_{c,p}\) measures the export in US dollars. The framework of the Economic-Complexity 19 , 20 , 21 , 22 based on the interaction between countries and products is expressed by the application of the Revealed Comparative Advantage (RCA) 28 threshold over the entries \({\tilde{m}}_{c,p}\) :

Finally, we define the entries of the biadjacency matrix M of the undirected bipartite network analyzed in this work as:

This indicates that the connection (country-product link) is established if and only if the relative RCA is relevant (over the threshold), otherwise it can be ignored. Each row of M represents the export basket of a given country (or its diversification \(k_c\) ), while each column represents the subset of producers of a given product (or its ubiquity \(k_p\) ) 52 .

Urbanization

The data of the urban population from 1995 to 2010 are available at the World Bank database ( https://data.worldbank.org/ Date of access: 18/01/2021).

Fitness and complexity

Fitness and Complexity are a metric for countries and products applied to bipartite binary matrix M of the WTW 19 , 20 , 21 , 22 , 24 . The basic idea of EC is to define a non-linear map through an iterative process which couples the Fitness of countries to the Complexity of products. At every step of the iteration, the Fitness \(F_c\) of a given country c is proportional to the sum of the exported products, weighted by their complexity parameter \(Q_p\) . In particular, the Fitness \(F_c\) for the generic country c and Quality \(Q_p\) for the generic product p at the \(n-\) th step of iteration, are defined as:

where the symbols \(\langle \cdot \rangle\) indicate the average taken over the proper set. The initial condition are taken as \(F_c^0=Q_p^0=1\,\,\forall c\in N_c,\,\forall p\in N_p\) , where \(N_c\) and \(N_p\) are the number respectively of countries and products (the convergence of the algorithm described by Eq. ( 4 ) depends on the shape of the matrix M , as it has been discussed in 43 ).

Bipartite configuration model (BICM)

The Bipartite Configuration Model (BICM), as defined by 34 , 35 , is a null model of general applicability that is able to generate a grandcanonical ensemble of bipartite, undirected, binary networks in which the two layers Country and Products have respectively C and P nodes. The ensemble generate by the BICM constrained the number of connections for each node, on both layers (in our case \(d_c\) and \(u_p\) ) to match, on average, the observed one. Each network \({\mathbf {M}}\) in such ensemble is assigned a probability coefficient:

\(x_c\) and \(y_s\) are the Lagrange multipliers associated to the constrained degrees.

Constraining the ensemble average values of countries and products degree induces the probability that a link exists between country c and industry sector p independently of the other links:

The numerical values of the unknown parameters \({\mathbf {x}}\) and \({\mathbf {y}}\) have to be determined by solving the following system of \(C+P\) equations, which constrains the ensemble average values of countries diversification and products ubiquities to match the real values, \(\langle d_c\rangle =d_c^*,\,c=1\dots C\) and \(\langle u_p\rangle =u_p^*,\,p=1\dots P\) .

Where \(\{d_c^*\}_{c=1}^C\) and \(\{u_p^*\}_{p=1}^S\) are the real degree sequence of countries, and industry sectors respectively, and \(\langle \cdot \rangle\) represents the ensemble average of a given quantity, over the ensemble measure defined by Eq. ( 6 )—as \(\langle d_c\rangle =\sum _sp_{cp}\) and \(\langle u_s\rangle =\sum _cp_{cp}\) . Indicated with an asterisk, “ \(*\) ” are the parameters that satisfy the systems.

Similarity motifs

In the present work we have sampled the grand canonical ensemble of binary, undirected, bipartite networks induced by the BiCM, according to the probability coefficients \(P({\mathbf {M}}|{\mathbf {x}}^*, {\mathbf {y}}^*)\) and calculated the average and variance of the motif \(\mu _{\text{ sim }}\) , define as b-motif6 in 40 .

The Similarity Motif represents the symmetric and complete connections between two countries \(c,c'\) and two industry sectors \(p,p'\) . The number of similarity motifs is:

with \({\fancyscript {Z}}\) is the matrix of dimension ( C ,  C ), that represents the projection of M over the countries. Each entry \({\fancyscript {Z}}_{cc'}\) counts the number of industry sectors in common between the countries c and \(c'\) , it is defined as: \({\fancyscript {Z}}_{cc'}=\sum _{s=1}^S M_{cs}M_{c's}=MM^T\)

This motif represents the co-occurrence of two products in two countries’ export basket within the bipatite matrix of the country exports. The accuracy of the BiCM prediction in reproducing the value of quantity \(\mu _{sim}\) please follows 34 .

Slope coefficient of a sliding window across \(25\%\) of the countries (corresponding to 36 countries) of respectively its Fitness Growth Rate ( A )— GDP Growth Rate ( B )— GDP Ranking Growth Rate ( C ) versus Urban Population Growth Rate. The error bar corresponds to the fit’s \(95\%\) confidence interval. The colors follow the Urban Range scheme.

Urban range versus fitness and GDP

To validate our analysis of the relation between the country’s fitness and the urbanization process we analyzed the urbanization growth rate in relation to the growth rate of three different metrics: the country Fitness (Fig.  5 A), country GDP (Fig.  5 B), and country GDP ranking (Fig.  5 C) between 1995 and 2010.

We study the variation of the slope coefficient of a sliding window across \(25\%\) of the countries urban range and the three metrics above. Both the metrics extracted from the GDP do not have statistical significant results. Although the growth rate of fitness in relation with the urbanization growth rate manifests a linear relation (Fig.  5 A) with an \(R^2=0.53\) , as Fig.  3 B, we notice that the fitness ranking is a more reliable tool than the raw fitness value 43 . The fitness ranking provides a more stable metric across each sliding window.

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Acknowledgements

Riccardo Di Clemente as Newton International Fellow of the Royal Society acknowledges support from the Royal Society, the British Academy, and the Academy of Medical Sciences (Newton International Fellowship, NF170505). The authors would like to thank Fabio Saracco, Enrico Ubaldi, Bernardo Monechi, Andrea Zaccaria, Andrea Gabrielli, Luciano Pietronero and Marta C. González for the insightful discussions and comments.

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Economic and Urban Dynamics: Investigating Socioeconomic Status and Urban Density as Moderators of Mobile Wallet Adoption in Smart Cities

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This research paper examines the complex correlation between socioeconomic factors, urban density, and the acceptance of mobile wallet technology in smart cities. The study investigates how socioeconomic status and urban density influence the adoption of mobile wallets. Smart cities have experienced a significant increase in the adoption of mobile payment solutions such as Apple Pay and Google Pay, noted for their technological innovation and ability to enhance living standards. These digital payment platforms provide ease, security, and efficiency, revolutionising how individuals engage in financial transactions and navigate urban environments. The study examines the many aspects that impact this phenomenon, focusing on the significance of comprehending how socioeconomic status and urban density influence the acceptance of mobile wallets. The study utilises a meticulous research technique, which involves evaluating the reliability and validity of constructs, analysing heterotrait-monotrait (HTMT) ratios, conducting tests for discriminant validity, and doing variance inflation factor (VIF) analysis. These measures are taken to ensure the strength and reliability of the report’s conclusions. The research’s importance is further supported by model fit statistics and hypothesis testing conducted through bootstrapping. The results emphasise that the inclusion of mobile wallet functions, the legal framework, and the development of smart city infrastructure have a substantial influence on the acceptance of mobile wallets. However, the impact of urban density on mobile wallet adoption is more intricate and multifaceted. This study provides significant insights into the dynamic field of technology uptake in urban regions, with implications for politicians, entrepreneurs, and urban planners seeking to promote financial inclusion and technological integration in smart cities.

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Kumar, P., Reepu, Kaur, R. (2024). Economic and Urban Dynamics: Investigating Socioeconomic Status and Urban Density as Moderators of Mobile Wallet Adoption in Smart Cities. In: Senjyu, T., So–In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. SmartCom 2024 2024. Lecture Notes in Networks and Systems, vol 948. Springer, Singapore. https://doi.org/10.1007/978-981-97-1329-5_33

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70 years after brown v. board of education, new research shows rise in school segregation.

As the nation prepares to mark the 70th anniversary of the landmark U.S. Supreme Court ruling in Brown v. Board of Education , a new report from researchers at Stanford and USC shows that racial and economic segregation among schools has grown steadily in large school districts over the past three decades — an increase that appears to be driven in part by policies favoring school choice over integration.

Analyzing data from U.S. public schools going back to 1967, the researchers found that segregation between white and Black students has increased by 64 percent since 1988 in the 100 largest districts, and segregation by economic status has increased by about 50 percent since 1991.

The report also provides new evidence about the forces driving recent trends in school segregation, showing that the expansion of charter schools has played a major role.  

The findings were released on May 6 with the launch of the Segregation Explorer , a new interactive website from the Educational Opportunity Project at Stanford University. The website provides searchable data on racial and economic school segregation in U.S. states, counties, metropolitan areas, and school districts from 1991 to 2022. 

“School segregation levels are not at pre- Brown levels, but they are high and have been rising steadily since the late 1980s,” said Sean Reardon , the Professor of Poverty and Inequality in Education at Stanford Graduate School of Education and faculty director of the Educational Opportunity Project. “In most large districts, school segregation has increased while residential segregation and racial economic inequality have declined, and our findings indicate that policy choices – not demographic changes – are driving the increase.” 

“There’s a tendency to attribute segregation in schools to segregation in neighborhoods,” said Ann Owens , a professor of sociology and public policy at USC. “But we’re finding that the story is more complicated than that.”

Assessing the rise

In the Brown v. Board decision issued on May 17, 1954, the U.S. Supreme Court ruled that racially segregated public schools violated the Equal Protection Clause of the Fourteenth Amendment and established that “separate but equal” schools were not only inherently unequal but unconstitutional. The ruling paved the way for future decisions that led to rapid school desegregation in many school districts in the late 1960s and early 1970s.

Though segregation in most school districts is much lower than it was 60 years ago, the researchers found that over the past three decades, both racial and economic segregation in large districts increased. Much of the increase in economic segregation since 1991, measured by segregation between students eligible and ineligible for free lunch, occurred in the last 15 years.

White-Hispanic and white-Asian segregation, while lower on average than white-Black segregation, have both more than doubled in large school districts since the 1980s. 

Racial-economic segregation – specifically the difference in the proportion of free-lunch-eligible students between the average white and Black or Hispanic student’s schools – has increased by 70 percent since 1991. 

School segregation is strongly associated with achievement gaps between racial and ethnic groups, especially the rate at which achievement gaps widen during school, the researchers said.  

“Segregation appears to shape educational outcomes because it concentrates Black and Hispanic students in higher-poverty schools, which results in unequal learning opportunities,” said Reardon, who is also a senior fellow at the Stanford Institute for Economic Policy Research and a faculty affiliate of the Stanford Accelerator for Learning . 

Policies shaping recent trends 

The recent rise in school segregation appears to be the direct result of educational policy and legal decisions, the researchers said. 

Both residential segregation and racial disparities in income declined between 1990 and 2020 in most large school districts. “Had nothing else changed, that trend would have led to lower school segregation,” said Owens. 

But since 1991, roughly two-thirds of districts that were under court-ordered desegregation have been released from court oversight. Meanwhile, since 1998, the charter sector – a form of expanded school choice – has grown.

Expanding school choice could influence segregation levels in different ways: If families sought schools that were more diverse than the ones available in their neighborhood, it could reduce segregation. But the researchers found that in districts where the charter sector expanded most rapidly in the 2000s and 2010s, segregation grew the most. 

The researchers’ analysis also quantified the extent to which the release from court orders accounted for the rise in school segregation. They found that, together, the release from court oversight and the expansion of choice accounted entirely for the rise in school segregation from 2000 to 2019.

The researchers noted enrollment policies that school districts can implement to mitigate segregation, such as voluntary integration programs, socioeconomic-based student assignment policies, and school choice policies that affirmatively promote integration. 

“School segregation levels are high, troubling, and rising in large districts,” said Reardon. “These findings should sound an alarm for educators and policymakers.”

Additional collaborators on the project include Demetra Kalogrides, Thalia Tom, and Heewon Jang. This research, including the development of the Segregation Explorer data and website, was supported by the Russell Sage Foundation, the Robert Wood Johnson Foundation, and the Bill and Melinda Gates Foundation.   

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Are Markups Driving the Ups and Downs of Inflation?

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FRBSF Economic Letter 2024-12 | May 13, 2024

How much impact have price markups for goods and services had on the recent surge and the subsequent decline of inflation? Since 2021, markups have risen substantially in a few industries such as motor vehicles and petroleum. However, aggregate markups—which are more relevant for overall inflation—have generally remained flat, in line with previous economic recoveries over the past three decades. These patterns suggest that markup fluctuations have not been a main driver of the ups and downs of inflation during the post-pandemic recovery.

In the recovery from the pandemic, U.S. inflation surged to a peak of over 7% in June 2022 and has since declined to 2.7% in March 2024, as measured by the 12-month change in the personal consumption expenditures (PCE) price index. What factors have been driving the ups and downs of inflation? Production costs are traditionally considered a main contributor, particularly costs stemming from fluctuations in demand for and supply of goods and services. As demand for their products rises, companies need to hire more workers and buy more intermediate goods, pushing up production costs. Supply chain disruptions can also push up the cost of production. Firms may pass on all or part of the cost increases to consumers by raising prices. Thus, an important theoretical linkage runs from cost increases to inflation. Likewise, decreases in costs should lead to disinflation.

Labor costs are an important factor of production costs and are often useful for gauging inflationary pressures. However, during the post-pandemic surge in inflation, nominal wages rose more slowly than prices, such that real labor costs were falling until early 2023. By contrast, disruptions to global supply chains pushed up intermediate goods costs, contributing to the surge in inflation (see, for example, Liu and Nguyen 2023). However, supply chains have more direct impacts on goods inflation than on services inflation, which also rose substantially.

In this Economic Letter , we consider another factor that might drive inflation fluctuations: changes in firms’ pricing power and markups. An increase in pricing power would be reflected in price-cost markups, leading to higher inflation; likewise, a decline in pricing power and markups could alleviate inflation pressures. We use industry-level measures of markups to trace their evolving impact on inflation during the current expansion. We find that markups rose substantially in some sectors, such as the motor vehicles industry. However, the aggregate markup across all sectors of the economy, which is more relevant for inflation, has stayed essentially flat during the post-pandemic recovery. This is broadly in line with patterns during previous business cycle recoveries. Overall, our analysis suggests that fluctuations in markups were not a main driver of the post-pandemic surge in inflation, nor of the recent disinflation that started in mid-2022.

Potential drivers of inflation: Production costs and markups

To support households and businesses during the pandemic, the Federal Reserve lowered the federal funds rate target to essentially zero, and the federal government provided large fiscal transfers and increased unemployment benefits. These policies boosted demand for goods and services, especially as the economy recovered from the depth of the pandemic.

The increase in overall demand, combined with supply shortages, boosted the costs of production, contributing to the surge in inflation during the post-pandemic recovery. Although labor costs account for a large part of firms’ total production costs, real labor costs were falling between early 2021 and mid-2022 such that the increases in prices outpaced those in nominal wages. This makes it unlikely that labor costs were driving the surge in inflation.

Instead, we focus on another potential alternative driver of inflation that resulted from firms’ ability to adjust prices, known as pricing power. As demand for goods surged early in the post-pandemic recovery, companies may have had a greater ability to raise their prices above their production costs, a gap known as markups. Following a sharp drop in spending at the height of the pandemic, people may have become eager to resume normal spending patterns and hence more tolerant to price increases than in the past. In fact, growth of nonfinancial corporate profits accelerated in the early part of the recovery (see Figure 1), suggesting that companies had increased pricing power. Some studies have pointed to the strong growth in nonfinancial corporate profits in 2021 as evidence that increased markups have contributed to inflation (see, for example, Weber and Wasmer 2023). However, the figure also shows that growth in corporate profits is typically volatile. Corporate profits tend to rise in the early stages of economic recoveries. Data for the current recovery show that the increase in corporate profits is not particularly pronounced compared with previous recoveries.

Figure 1 Profit growth for nonfinancial businesses

More importantly, corporate profits are an imperfect measure of a firm’s pricing power because several other factors can drive changes in profitability. For instance, much of the recent rise in corporate profits can be attributed to lower business taxes and higher subsidies from pandemic-related government support, as well as lower net interest payments due to monetary policy accommodation (Pallazzo 2023).

Instead of relying on profits as a measure of pricing power, we construct direct measures of markups based on standard economic models. Theory suggests that companies set prices as a markup over variable production costs, and that markup can be inferred from the share of a firm’s revenue spent on a given variable production factor, such as labor or intermediate goods. Over the period of data we use, we assume that the specific proportion of a company’s production costs going toward inputs does not change. If the share of a firm’s revenue used for inputs falls, it would imply a rise in the firm’s price-cost margin or markup. In our main analysis, we use industry-level data from the Bureau of Economic Analysis (BEA) to compute markups based on the share of revenue spent on intermediate inputs. Our results are similar if we instead use the share of revenue going toward labor costs.

We compare the evolution of markups to that of prices, as measured by the PCE price index, since the recovery from the pandemic. In constructing this price index, the BEA takes into account changes in product characteristics (for instance, size) that could otherwise bias the inflation measure by comparing the prices of inherently different products over time. Similarly, based upon standard economic theory, our markup measure implicitly captures changes in those characteristics (see, for example, Aghion et al. 2023).

The post-pandemic evolution of markups

We examine the evolution of markups in each industry since the third quarter of 2020, the start of the post-pandemic recovery. Figure 2 shows that some sectors, such as the motor vehicles and petroleum industries, experienced large cumulative increases in markups during the recovery. Markups also rose substantially in general merchandise, such as department stores, and for other services, such as repair and maintenance, personal care, and laundry services. Since the start of the expansion, markups in those industries rose by over 10%—comparable in size to the cumulative increases over the same period in the core PCE price index, which excludes volatile food and energy components. However, the surge in inflation through June 2022 was broad based, with prices also rising substantially outside of these sectors. Thus, understanding the importance of markups for driving inflation requires a macroeconomic perspective that examines the evolution of aggregate markups across all sectors of the economy.

Figure 2 Cumulative changes in markups for salient industries

The role of aggregate markups in the economy

To assess how much markup changes contribute to movements in inflation more broadly, we use our industry-level measurements to calculate an aggregate markup at the macroeconomic level. We aggregate the cumulative changes in industry markups, applying two different weighting methods, as displayed in Figure 3. In the first method (green line), we match our industry categories to the spending categories in the core PCE price index for ease of comparison; we then use the PCE weights for each category to compute the aggregate markup. Alternatively, we use each industry’s cost weights to compute the aggregate markup (blue line). Regardless of the weighting method, Figure 3 shows that aggregate markups have stayed essentially flat since the start of the recovery, while the core PCE price index (gray line) rose by more than 10%. Thus, changes in markups are not likely to be the main driver of inflation during the recovery, which aligns with results from Glover, Mustre-del-Río, and von Ende-Becker (2023) and Hornstein (2023) using different methodologies or data. Markups also have not played much of a role in the slowing of inflation since the summer of 2022.

Figure 3 Cumulative changes in aggregate markups and prices

Moreover, the path of aggregate markups over the past three years is not unusual compared with previous recoveries. Figure 4 shows the cumulative changes in aggregate markups since the start of the current recovery (dark blue line), alongside aggregate markups following the 1991 (green line), 2001 (yellow line), and 2008 (light blue line) recessions. Aggregate markups have stayed roughly constant throughout all four recoveries.

Figure 4 Cumulative changes of aggregate markups in recoveries

Firms’ pricing power may change over time, resulting in markup fluctuations. In this Letter , we examine whether increases in markups played an important role during the inflation surge between early 2021 and mid-2022 and if declines in markups have contributed to disinflation since then. Using industry-level data, we show that markups did rise substantially in a few important sectors, such as motor vehicles and petroleum products. However, aggregate markups—the more relevant measure for overall inflation—have stayed essentially flat since the start of the recovery. As such, rising markups have not been a main driver of the recent surge and subsequent decline in inflation during the current recovery.

Aghion, Philippe, Antonin Bergeaud, Timo Boppart, Peter J. Klenow, and Huiyu Li. 2023. “A Theory of Falling Growth and Rising Rents.”  Review of Economic Studies  90(6), pp.2,675-2,702.

Glover, Andrew, José Mustre-del-Río, and Alice von Ende-Becker. 2023. “ How Much Have Record Corporate Profits Contributed to Recent Inflation? ” FRB Kansas City Economic Review 108(1).

Hornstein, Andreas. 2023. “ Profits and Inflation in the Time of Covid .” FRB Richmond Economic Brief 23-38 (November).

Liu, Zheng, and Thuy Lan Nguyen. 2023. “ Global Supply Chain Pressures and U.S. Inflation .” FRBSF Economic Letter 2023-14 (June 20).

Palazzo, Berardino. 2023. “ Corporate Profits in the Aftermath of COVID-19 .” FEDS Notes , Federal Reserve Board of Governors, September 8.

Weber, Isabella M. and Evan Wasner. 2023. “Sellers’ Inflation, Profits and Conflict: Why Can Large Firms Hike Prices in an Emergency?” Review of Keynesian Economics 11(2), pp. 183-213.

Opinions expressed in FRBSF Economic Letter do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco or of the Board of Governors of the Federal Reserve System. This publication is edited by Anita Todd and Karen Barnes. Permission to reprint portions of articles or whole articles must be obtained in writing. Please send editorial comments and requests for reprint permission to [email protected]